{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "1-3_transfer_learning_on_GoogleColab.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "350e40ad89fd4b289557201d12bd1878": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_44a3efe233ad48699e9f59747f120f8d",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_241cd1502c7c473ca0f525562f268c92",
              "IPY_MODEL_96edb32a99d04f7d8bb9f161a699d4d3"
            ]
          }
        },
        "44a3efe233ad48699e9f59747f120f8d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "241cd1502c7c473ca0f525562f268c92": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_1b891dddf11343e696e5fd695c5a71df",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 553433881,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 553433881,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_bc11034b13794c8db6b5dc2f4543932a"
          }
        },
        "96edb32a99d04f7d8bb9f161a699d4d3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_d6b27aef63334910b4ec0b78b666684d",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 528M/528M [00:14&lt;00:00, 39.5MB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_af72d2b92986469d8abb55ab7b2ca180"
          }
        },
        "1b891dddf11343e696e5fd695c5a71df": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "bc11034b13794c8db6b5dc2f4543932a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "d6b27aef63334910b4ec0b78b666684d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "af72d2b92986469d8abb55ab7b2ca180": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UA1x5l3QlWqC"
      },
      "source": [
        "# 1.3「転移学習」で少量データの分類を実現する方法\n",
        "\n",
        "- 本ファイルでは、学習済みのVGGモデルを使用し、転移学習でアリとハチの画像を分類するモデルを学習します\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRJbymzfozM5"
      },
      "source": [
        "## 本ファイルは、Google Colabでの実行を前提としています"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82UCc0cflWqI"
      },
      "source": [
        "# 学習目標\n",
        "\n",
        "1. 画像データからDatasetを作成できるようになる\n",
        "2. DataSetからDataLoaderを作成できるようになる\n",
        "3. 学習済みモデルの出力層を任意の形に変更できるようになる\n",
        "4. 出力層の結合パラメータのみを学習させ、転移学習が実装できるようになる\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJN48pwclWqJ"
      },
      "source": [
        "# 事前準備\n",
        "\n",
        "1. 書籍の指示に従い、本章で使用するデータをダウンロード\n",
        "\n",
        "2. forループの経過時間と残り時間を計測するパッケージtqdmをインストールします。\n",
        "\n",
        "conda install -c conda-forge tqdm\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWDnhY8Plo4P",
        "outputId": "9bf366b5-0c33-4f2f-a723-bcdc1f205d4d"
      },
      "source": [
        "!git clone https://github.com/YutaroOgawa/pytorch_advanced.git"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch_advanced'...\n",
            "remote: Enumerating objects: 479, done.\u001b[K\n",
            "remote: Total 479 (delta 0), reused 0 (delta 0), pack-reused 479\u001b[K\n",
            "Receiving objects: 100% (479/479), 14.75 MiB | 30.76 MiB/s, done.\n",
            "Resolving deltas: 100% (259/259), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLva_hyUlxlf",
        "outputId": "1a9528d2-539a-4296-9edb-7793cdd0b464"
      },
      "source": [
        "!ls\r\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pytorch_advanced  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dLafiH3l0v2",
        "outputId": "d3476745-7a8f-479f-caf3-42f1bc6a5377"
      },
      "source": [
        "%cd \"pytorch_advanced\""
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pytorch_advanced\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gw4aia4Dl3F2",
        "outputId": "1df93c9f-938b-4e8d-9f75-cbf721b02e30"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1_image_classification\t 7_nlp_sentiment_transformer\n",
            "2_objectdetection\t 8_nlp_sentiment_bert\n",
            "3_semantic_segmentation  9_video_classification_eco\n",
            "4_pose_estimation\t etc\n",
            "5_gan_generation\t LICENSE\n",
            "6_gan_anomaly_detection  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6LE69pul4zW",
        "outputId": "27b8663a-ebb5-4f09-d477-2d71cdd1d372"
      },
      "source": [
        "%cd \"1_image_classification\""
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pytorch_advanced/1_image_classification\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YIxC8nkmLgB",
        "outputId": "59315278-1a73-4e23-e654-2c2aeb6ec5a5"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1-1_load_vgg.ipynb\t     data\n",
            "1-3_transfer_learning.ipynb  make_folders_and_data_downloads.ipynb\n",
            "1-5_fine_tuning.ipynb\t     utils\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teXfeIJxmMkw"
      },
      "source": [
        "# make_folders_and_data_downloads.ipynbの中身を実行\r\n",
        "import os\r\n",
        "import urllib.request\r\n",
        "import zipfile\r\n",
        "\r\n",
        "\r\n",
        "data_dir = \"./data/\"\r\n",
        "if not os.path.exists(data_dir):\r\n",
        "    os.mkdir(data_dir)\r\n",
        "\r\n",
        "url = \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\"\r\n",
        "save_path = os.path.join(data_dir, \"imagenet_class_index.json\")\r\n",
        "\r\n",
        "if not os.path.exists(save_path):\r\n",
        "    urllib.request.urlretrieve(url, save_path)\r\n",
        "\r\n",
        "url = \"https://download.pytorch.org/tutorial/hymenoptera_data.zip\"\r\n",
        "save_path = os.path.join(data_dir, \"hymenoptera_data.zip\")\r\n",
        "\r\n",
        "if not os.path.exists(save_path):\r\n",
        "    urllib.request.urlretrieve(url, save_path)\r\n",
        "\r\n",
        "    # ZIPファイルを読み込み\r\n",
        "    zip = zipfile.ZipFile(save_path)\r\n",
        "    zip.extractall(data_dir)  # ZIPを解凍\r\n",
        "    zip.close()  # ZIPファイルをクローズ\r\n",
        "\r\n",
        "    # ZIPファイルを消去\r\n",
        "    os.remove(save_path)\r\n",
        "\r\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4M1yJuxmVyp"
      },
      "source": [
        "# 追記以上--------------------------"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Nk2_bfilWqJ"
      },
      "source": [
        "# パッケージのimport\n",
        "import glob\n",
        "import os.path as osp\n",
        "import random\n",
        "import numpy as np\n",
        "import json\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "import torchvision\n",
        "from torchvision import models, transforms\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsQFy7nflWqK"
      },
      "source": [
        "# 乱数のシードを設定\n",
        "torch.manual_seed(1234)\n",
        "np.random.seed(1234)\n",
        "random.seed(1234)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0wuE1LQlWqK"
      },
      "source": [
        "# DataSetを作成"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PerlS6ilWqK"
      },
      "source": [
        "# 入力画像の前処理をするクラス\n",
        "# 訓練時と推論時で処理が異なる\n",
        "\n",
        "\n",
        "class ImageTransform():\n",
        "    \"\"\"\n",
        "    画像の前処理クラス。訓練時、検証時で異なる動作をする。\n",
        "    画像のサイズをリサイズし、色を標準化する。\n",
        "    訓練時はRandomResizedCropとRandomHorizontalFlipでデータオーギュメンテーションする。\n",
        "\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "    resize : int\n",
        "        リサイズ先の画像の大きさ。\n",
        "    mean : (R, G, B)\n",
        "        各色チャネルの平均値。\n",
        "    std : (R, G, B)\n",
        "        各色チャネルの標準偏差。\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, resize, mean, std):\n",
        "        self.data_transform = {\n",
        "            'train': transforms.Compose([\n",
        "                transforms.RandomResizedCrop(\n",
        "                    resize, scale=(0.5, 1.0)),  # データオーギュメンテーション\n",
        "                transforms.RandomHorizontalFlip(),  # データオーギュメンテーション\n",
        "                transforms.ToTensor(),  # テンソルに変換\n",
        "                transforms.Normalize(mean, std)  # 標準化\n",
        "            ]),\n",
        "            'val': transforms.Compose([\n",
        "                transforms.Resize(resize),  # リサイズ\n",
        "                transforms.CenterCrop(resize),  # 画像中央をresize×resizeで切り取り\n",
        "                transforms.ToTensor(),  # テンソルに変換\n",
        "                transforms.Normalize(mean, std)  # 標準化\n",
        "            ])\n",
        "        }\n",
        "\n",
        "    def __call__(self, img, phase='train'):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        phase : 'train' or 'val'\n",
        "            前処理のモードを指定。\n",
        "        \"\"\"\n",
        "        return self.data_transform[phase](img)\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "mnzGcMN_lWqL",
        "outputId": "f7d4f271-7c1f-4028-a5fe-9c1d1721218d"
      },
      "source": [
        "# 訓練時の画像前処理の動作を確認\n",
        "# 実行するたびに処理結果の画像が変わる\n",
        "\n",
        "# 1. 画像読み込み\n",
        "image_file_path = './data/goldenretriever-3724972_640.jpg'\n",
        "img = Image.open(image_file_path)   # [高さ][幅][色RGB]\n",
        "\n",
        "# 2. 元の画像の表示\n",
        "plt.imshow(img)\n",
        "plt.show()\n",
        "\n",
        "# 3. 画像の前処理と処理済み画像の表示\n",
        "size = 224\n",
        "mean = (0.485, 0.456, 0.406)\n",
        "std = (0.229, 0.224, 0.225)\n",
        "\n",
        "transform = ImageTransform(size, mean, std)\n",
        "img_transformed = transform(img, phase=\"train\")  # torch.Size([3, 224, 224])\n",
        "\n",
        "# (色、高さ、幅)を (高さ、幅、色)に変換し、0-1に値を制限して表示\n",
        "img_transformed = img_transformed.numpy().transpose((1, 2, 0))\n",
        "img_transformed = np.clip(img_transformed, 0, 1)\n",
        "plt.imshow(img_transformed)\n",
        "plt.show()\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3gJ3XmJlWqM",
        "outputId": "23617ca5-dd0c-4bba-bfd7-e41fc270607e"
      },
      "source": [
        "# アリとハチの画像へのファイルパスのリストを作成する\n",
        "\n",
        "\n",
        "def make_datapath_list(phase=\"train\"):\n",
        "    \"\"\"\n",
        "    データのパスを格納したリストを作成する。\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    phase : 'train' or 'val'\n",
        "        訓練データか検証データかを指定する\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    path_list : list\n",
        "        データへのパスを格納したリスト\n",
        "    \"\"\"\n",
        "\n",
        "    rootpath = \"./data/hymenoptera_data/\"\n",
        "    target_path = osp.join(rootpath+phase+'/**/*.jpg')\n",
        "    print(target_path)\n",
        "\n",
        "    path_list = []  # ここに格納する\n",
        "\n",
        "    # globを利用してサブディレクトリまでファイルパスを取得する\n",
        "    for path in glob.glob(target_path):\n",
        "        path_list.append(path)\n",
        "\n",
        "    return path_list\n",
        "\n",
        "\n",
        "# 実行\n",
        "train_list = make_datapath_list(phase=\"train\")\n",
        "val_list = make_datapath_list(phase=\"val\")\n",
        "\n",
        "train_list\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./data/hymenoptera_data/train/**/*.jpg\n",
            "./data/hymenoptera_data/val/**/*.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['./data/hymenoptera_data/train/ants/9715481_b3cb4114ff.jpg',\n",
              " './data/hymenoptera_data/train/ants/5650366_e22b7e1065.jpg',\n",
              " './data/hymenoptera_data/train/ants/1808777855_2a895621d7.jpg',\n",
              " './data/hymenoptera_data/train/ants/541630764_dbd285d63c.jpg',\n",
              " './data/hymenoptera_data/train/ants/kurokusa.jpg',\n",
              " './data/hymenoptera_data/train/ants/2288450226_a6e96e8fdf.jpg',\n",
              " './data/hymenoptera_data/train/ants/2265824718_2c96f485da.jpg',\n",
              " './data/hymenoptera_data/train/ants/318052216_84dff3f98a.jpg',\n",
              " './data/hymenoptera_data/train/ants/384191229_5779cf591b.jpg',\n",
              " './data/hymenoptera_data/train/ants/795000156_a9900a4a71.jpg',\n",
              " './data/hymenoptera_data/train/ants/540889389_48bb588b21.jpg',\n",
              " './data/hymenoptera_data/train/ants/1030023514_aad5c608f9.jpg',\n",
              " './data/hymenoptera_data/train/ants/2292213964_ca51ce4bef.jpg',\n",
              " './data/hymenoptera_data/train/ants/Nepenthes_rafflesiana_ant.jpg',\n",
              " './data/hymenoptera_data/train/ants/1804095607_0341701e1c.jpg',\n",
              " './data/hymenoptera_data/train/ants/VietnameseAntMimicSpider.jpg',\n",
              " './data/hymenoptera_data/train/ants/886401651_f878e888cd.jpg',\n",
              " './data/hymenoptera_data/train/ants/474806473_ca6caab245.jpg',\n",
              " './data/hymenoptera_data/train/ants/938946700_ca1c669085.jpg',\n",
              " './data/hymenoptera_data/train/ants/0013035.jpg',\n",
              " './data/hymenoptera_data/train/ants/6240338_93729615ec.jpg',\n",
              " './data/hymenoptera_data/train/ants/374435068_7eee412ec4.jpg',\n",
              " './data/hymenoptera_data/train/ants/518773929_734dbc5ff4.jpg',\n",
              " './data/hymenoptera_data/train/ants/45472593_bfd624f8dc.jpg',\n",
              " './data/hymenoptera_data/train/ants/1917341202_d00a7f9af5.jpg',\n",
              " './data/hymenoptera_data/train/ants/swiss-army-ant.jpg',\n",
              " './data/hymenoptera_data/train/ants/403746349_71384f5b58.jpg',\n",
              " './data/hymenoptera_data/train/ants/1924473702_daa9aacdbe.jpg',\n",
              " './data/hymenoptera_data/train/ants/255434217_1b2b3fe0a4.jpg',\n",
              " './data/hymenoptera_data/train/ants/24335309_c5ea483bb8.jpg',\n",
              " './data/hymenoptera_data/train/ants/201558278_fe4caecc76.jpg',\n",
              " './data/hymenoptera_data/train/ants/20935278_9190345f6b.jpg',\n",
              " './data/hymenoptera_data/train/ants/424873399_47658a91fb.jpg',\n",
              " './data/hymenoptera_data/train/ants/69639610_95e0de17aa.jpg',\n",
              " './data/hymenoptera_data/train/ants/army-ants-red-picture.jpg',\n",
              " './data/hymenoptera_data/train/ants/424119020_6d57481dab.jpg',\n",
              " './data/hymenoptera_data/train/ants/522415432_2218f34bf8.jpg',\n",
              " './data/hymenoptera_data/train/ants/258217966_d9d90d18d3.jpg',\n",
              " './data/hymenoptera_data/train/ants/450057712_771b3bfc91.jpg',\n",
              " './data/hymenoptera_data/train/ants/466430434_4000737de9.jpg',\n",
              " './data/hymenoptera_data/train/ants/2278278459_6b99605e50.jpg',\n",
              " './data/hymenoptera_data/train/ants/6743948_2b8c096dda.jpg',\n",
              " './data/hymenoptera_data/train/ants/1262877379_64fcada201.jpg',\n",
              " './data/hymenoptera_data/train/ants/207947948_3ab29d7207.jpg',\n",
              " './data/hymenoptera_data/train/ants/1368913450_e146e2fb6d.jpg',\n",
              " './data/hymenoptera_data/train/ants/506249802_207cd979b4.jpg',\n",
              " './data/hymenoptera_data/train/ants/560966032_988f4d7bc4.jpg',\n",
              " './data/hymenoptera_data/train/ants/1360291657_dc248c5eea.jpg',\n",
              " './data/hymenoptera_data/train/ants/36439863_0bec9f554f.jpg',\n",
              " './data/hymenoptera_data/train/ants/408393566_b5b694119b.jpg',\n",
              " './data/hymenoptera_data/train/ants/116570827_e9c126745d.jpg',\n",
              " './data/hymenoptera_data/train/ants/1660097129_384bf54490.jpg',\n",
              " './data/hymenoptera_data/train/ants/49375974_e28ba6f17e.jpg',\n",
              " './data/hymenoptera_data/train/ants/334167043_cbd1adaeb9.jpg',\n",
              " './data/hymenoptera_data/train/ants/67270775_e9fdf77e9d.jpg',\n",
              " './data/hymenoptera_data/train/ants/522163566_fec115ca66.jpg',\n",
              " './data/hymenoptera_data/train/ants/167890289_dd5ba923f3.jpg',\n",
              " './data/hymenoptera_data/train/ants/339670531_94b75ae47a.jpg',\n",
              " './data/hymenoptera_data/train/ants/531979952_bde12b3bc0.jpg',\n",
              " './data/hymenoptera_data/train/ants/ant photos.jpg',\n",
              " './data/hymenoptera_data/train/ants/543417860_b14237f569.jpg',\n",
              " './data/hymenoptera_data/train/ants/28847243_e79fe052cd.jpg',\n",
              " './data/hymenoptera_data/train/ants/1095476100_3906d8afde.jpg',\n",
              " './data/hymenoptera_data/train/ants/6240329_72c01e663e.jpg',\n",
              " './data/hymenoptera_data/train/ants/841049277_b28e58ad05.jpg',\n",
              " './data/hymenoptera_data/train/ants/149244013_c529578289.jpg',\n",
              " './data/hymenoptera_data/train/ants/7759525_1363d24e88.jpg',\n",
              " './data/hymenoptera_data/train/ants/506249836_717b73f540.jpg',\n",
              " './data/hymenoptera_data/train/ants/892108839_f1aad4ca46.jpg',\n",
              " './data/hymenoptera_data/train/ants/245647475_9523dfd13e.jpg',\n",
              " './data/hymenoptera_data/train/ants/150801003_3390b73135.jpg',\n",
              " './data/hymenoptera_data/train/ants/2288481644_83ff7e4572.jpg',\n",
              " './data/hymenoptera_data/train/ants/2265825502_fff99cfd2d.jpg',\n",
              " './data/hymenoptera_data/train/ants/trap-jaw-ant-insect-bg.jpg',\n",
              " './data/hymenoptera_data/train/ants/1489674356_09d48dde0a.jpg',\n",
              " './data/hymenoptera_data/train/ants/707895295_009cf23188.jpg',\n",
              " './data/hymenoptera_data/train/ants/196057951_63bf063b92.jpg',\n",
              " './data/hymenoptera_data/train/ants/460874319_0a45ab4d05.jpg',\n",
              " './data/hymenoptera_data/train/ants/MehdiabadiAnt2_600.jpg',\n",
              " './data/hymenoptera_data/train/ants/82852639_52b7f7f5e3.jpg',\n",
              " './data/hymenoptera_data/train/ants/386190770_672743c9a7.jpg',\n",
              " './data/hymenoptera_data/train/ants/termite-vs-ant.jpg',\n",
              " './data/hymenoptera_data/train/ants/382971067_0bfd33afe0.jpg',\n",
              " './data/hymenoptera_data/train/ants/1225872729_6f0856588f.jpg',\n",
              " './data/hymenoptera_data/train/ants/132478121_2a430adea2.jpg',\n",
              " './data/hymenoptera_data/train/ants/1269756697_0bce92cdab.jpg',\n",
              " './data/hymenoptera_data/train/ants/460372577_f2f6a8c9fc.jpg',\n",
              " './data/hymenoptera_data/train/ants/201790779_527f4c0168.jpg',\n",
              " './data/hymenoptera_data/train/ants/649026570_e58656104b.jpg',\n",
              " './data/hymenoptera_data/train/ants/154124431_65460430f2.jpg',\n",
              " './data/hymenoptera_data/train/ants/178538489_bec7649292.jpg',\n",
              " './data/hymenoptera_data/train/ants/684133190_35b62c0c1d.jpg',\n",
              " './data/hymenoptera_data/train/ants/150801171_cd86f17ed8.jpg',\n",
              " './data/hymenoptera_data/train/ants/196757565_326437f5fe.jpg',\n",
              " './data/hymenoptera_data/train/ants/224655713_3956f7d39a.jpg',\n",
              " './data/hymenoptera_data/train/ants/535522953_308353a07c.jpg',\n",
              " './data/hymenoptera_data/train/ants/1693954099_46d4c20605.jpg',\n",
              " './data/hymenoptera_data/train/ants/1099452230_d1949d3250.jpg',\n",
              " './data/hymenoptera_data/train/ants/hormiga_co_por.jpg',\n",
              " './data/hymenoptera_data/train/ants/822537660_caf4ba5514.jpg',\n",
              " './data/hymenoptera_data/train/ants/148715752_302c84f5a4.jpg',\n",
              " './data/hymenoptera_data/train/ants/662541407_ff8db781e7.jpg',\n",
              " './data/hymenoptera_data/train/ants/512863248_43c8ce579b.jpg',\n",
              " './data/hymenoptera_data/train/ants/275429470_b2d7d9290b.jpg',\n",
              " './data/hymenoptera_data/train/ants/459694881_ac657d3187.jpg',\n",
              " './data/hymenoptera_data/train/ants/2019439677_2db655d361.jpg',\n",
              " './data/hymenoptera_data/train/ants/512164029_c0a66b8498.jpg',\n",
              " './data/hymenoptera_data/train/ants/392382602_1b7bed32fa.jpg',\n",
              " './data/hymenoptera_data/train/ants/188552436_605cc9b36b.jpg',\n",
              " './data/hymenoptera_data/train/ants/533848102_70a85ad6dd.jpg',\n",
              " './data/hymenoptera_data/train/ants/226951206_d6bf946504.jpg',\n",
              " './data/hymenoptera_data/train/ants/Ant_1.jpg',\n",
              " './data/hymenoptera_data/train/ants/342438950_a3da61deab.jpg',\n",
              " './data/hymenoptera_data/train/ants/1286984635_5119e80de1.jpg',\n",
              " './data/hymenoptera_data/train/ants/175998972.jpg',\n",
              " './data/hymenoptera_data/train/ants/957233405_25c1d1187b.jpg',\n",
              " './data/hymenoptera_data/train/ants/1473187633_63ccaacea6.jpg',\n",
              " './data/hymenoptera_data/train/ants/470127037_513711fd21.jpg',\n",
              " './data/hymenoptera_data/train/ants/998118368_6ac1d91f81.jpg',\n",
              " './data/hymenoptera_data/train/ants/162603798_40b51f1654.jpg',\n",
              " './data/hymenoptera_data/train/ants/484293231_e53cfc0c89.jpg',\n",
              " './data/hymenoptera_data/train/ants/475961153_b8c13fd405.jpg',\n",
              " './data/hymenoptera_data/train/bees/365759866_b15700c59b.jpg',\n",
              " './data/hymenoptera_data/train/bees/465133211_80e0c27f60.jpg',\n",
              " './data/hymenoptera_data/train/bees/279113587_b4843db199.jpg',\n",
              " './data/hymenoptera_data/train/bees/2645107662_b73a8595cc.jpg',\n",
              " './data/hymenoptera_data/train/bees/537309131_532bfa59ea.jpg',\n",
              " './data/hymenoptera_data/train/bees/36900412_92b81831ad.jpg',\n",
              " './data/hymenoptera_data/train/bees/3074585407_9854eb3153.jpg',\n",
              " './data/hymenoptera_data/train/bees/2610833167_79bf0bcae5.jpg',\n",
              " './data/hymenoptera_data/train/bees/3079610310_ac2d0ae7bc.jpg',\n",
              " './data/hymenoptera_data/train/bees/760526046_547e8b381f.jpg',\n",
              " './data/hymenoptera_data/train/bees/95238259_98470c5b10.jpg',\n",
              " './data/hymenoptera_data/train/bees/969455125_58c797ef17.jpg',\n",
              " './data/hymenoptera_data/train/bees/1295655112_7813f37d21.jpg',\n",
              " './data/hymenoptera_data/train/bees/473618094_8ffdcab215.jpg',\n",
              " './data/hymenoptera_data/train/bees/174142798_e5ad6d76e0.jpg',\n",
              " './data/hymenoptera_data/train/bees/2962405283_22718d9617.jpg',\n",
              " './data/hymenoptera_data/train/bees/29494643_e3410f0d37.jpg',\n",
              " './data/hymenoptera_data/train/bees/2610838525_fe8e3cae47.jpg',\n",
              " './data/hymenoptera_data/train/bees/1508176360_2972117c9d.jpg',\n",
              " './data/hymenoptera_data/train/bees/2495722465_879acf9d85.jpg',\n",
              " './data/hymenoptera_data/train/bees/342758693_c56b89b6b6.jpg',\n",
              " './data/hymenoptera_data/train/bees/3006264892_30e9cced70.jpg',\n",
              " './data/hymenoptera_data/train/bees/2683605182_9d2a0c66cf.jpg',\n",
              " './data/hymenoptera_data/train/bees/1799726602_8580867f71.jpg',\n",
              " './data/hymenoptera_data/train/bees/2722592222_258d473e17.jpg',\n",
              " './data/hymenoptera_data/train/bees/2822388965_f6dca2a275.jpg',\n",
              " './data/hymenoptera_data/train/bees/469333327_358ba8fe8a.jpg',\n",
              " './data/hymenoptera_data/train/bees/208702903_42fb4d9748.jpg',\n",
              " './data/hymenoptera_data/train/bees/452462695_40a4e5b559.jpg',\n",
              " './data/hymenoptera_data/train/bees/154600396_53e1252e52.jpg',\n",
              " './data/hymenoptera_data/train/bees/3090975720_71f12e6de4.jpg',\n",
              " './data/hymenoptera_data/train/bees/3030189811_01d095b793.jpg',\n",
              " './data/hymenoptera_data/train/bees/2486746709_c43cec0e42.jpg',\n",
              " './data/hymenoptera_data/train/bees/2861002136_52c7c6f708.jpg',\n",
              " './data/hymenoptera_data/train/bees/2445215254_51698ff797.jpg',\n",
              " './data/hymenoptera_data/train/bees/2486729079_62df0920be.jpg',\n",
              " './data/hymenoptera_data/train/bees/1232245714_f862fbe385.jpg',\n",
              " './data/hymenoptera_data/train/bees/90179376_abc234e5f4.jpg',\n",
              " './data/hymenoptera_data/train/bees/457457145_5f86eb7e9c.jpg',\n",
              " './data/hymenoptera_data/train/bees/2330918208_8074770c20.jpg',\n",
              " './data/hymenoptera_data/train/bees/444532809_9e931e2279.jpg',\n",
              " './data/hymenoptera_data/train/bees/2580598377_a4caecdb54.jpg',\n",
              " './data/hymenoptera_data/train/bees/2037437624_2d7bce461f.jpg',\n",
              " './data/hymenoptera_data/train/bees/1093831624_fb5fbe2308.jpg',\n",
              " './data/hymenoptera_data/train/bees/2617161745_fa3ebe85b4.jpg',\n",
              " './data/hymenoptera_data/train/bees/2493379287_4100e1dacc.jpg',\n",
              " './data/hymenoptera_data/train/bees/2470492904_837e97800d.jpg',\n",
              " './data/hymenoptera_data/train/bees/2321139806_d73d899e66.jpg',\n",
              " './data/hymenoptera_data/train/bees/446296270_d9e8b93ecf.jpg',\n",
              " './data/hymenoptera_data/train/bees/2638074627_6b3ae746a0.jpg',\n",
              " './data/hymenoptera_data/train/bees/2710368626_cb42882dc8.jpg',\n",
              " './data/hymenoptera_data/train/bees/873076652_eb098dab2d.jpg',\n",
              " './data/hymenoptera_data/train/bees/2551813042_8a070aeb2b.jpg',\n",
              " './data/hymenoptera_data/train/bees/3044402684_3853071a87.jpg',\n",
              " './data/hymenoptera_data/train/bees/2625499656_e3415e374d.jpg',\n",
              " './data/hymenoptera_data/train/bees/522104315_5d3cb2758e.jpg',\n",
              " './data/hymenoptera_data/train/bees/16838648_415acd9e3f.jpg',\n",
              " './data/hymenoptera_data/train/bees/2704348794_eb5d5178c2.jpg',\n",
              " './data/hymenoptera_data/train/bees/196430254_46bd129ae7.jpg',\n",
              " './data/hymenoptera_data/train/bees/2405441001_b06c36fa72.jpg',\n",
              " './data/hymenoptera_data/train/bees/129236073_0985e91c7d.jpg',\n",
              " './data/hymenoptera_data/train/bees/39747887_42df2855ee.jpg',\n",
              " './data/hymenoptera_data/train/bees/21399619_3e61e5bb6f.jpg',\n",
              " './data/hymenoptera_data/train/bees/85112639_6e860b0469.jpg',\n",
              " './data/hymenoptera_data/train/bees/2652877533_a564830cbf.jpg',\n",
              " './data/hymenoptera_data/train/bees/2634617358_f32fd16bea.jpg',\n",
              " './data/hymenoptera_data/train/bees/2031225713_50ed499635.jpg',\n",
              " './data/hymenoptera_data/train/bees/150013791_969d9a968b.jpg',\n",
              " './data/hymenoptera_data/train/bees/2358061370_9daabbd9ac.jpg',\n",
              " './data/hymenoptera_data/train/bees/205835650_e6f2614bee.jpg',\n",
              " './data/hymenoptera_data/train/bees/98391118_bdb1e80cce.jpg',\n",
              " './data/hymenoptera_data/train/bees/1691282715_0addfdf5e8.jpg',\n",
              " './data/hymenoptera_data/train/bees/17209602_fe5a5a746f.jpg',\n",
              " './data/hymenoptera_data/train/bees/586041248_3032e277a9.jpg',\n",
              " './data/hymenoptera_data/train/bees/774440991_63a4aa0cbe.jpg',\n",
              " './data/hymenoptera_data/train/bees/472288710_2abee16fa0.jpg',\n",
              " './data/hymenoptera_data/train/bees/359928878_b3b418c728.jpg',\n",
              " './data/hymenoptera_data/train/bees/354167719_22dca13752.jpg',\n",
              " './data/hymenoptera_data/train/bees/452462677_7be43af8ff.jpg',\n",
              " './data/hymenoptera_data/train/bees/2801728106_833798c909.jpg',\n",
              " './data/hymenoptera_data/train/bees/3100226504_c0d4f1e3f1.jpg',\n",
              " './data/hymenoptera_data/train/bees/513545352_fd3e7c7c5d.jpg',\n",
              " './data/hymenoptera_data/train/bees/2477349551_e75c97cf4d.jpg',\n",
              " './data/hymenoptera_data/train/bees/2792000093_e8ae0718cf.jpg',\n",
              " './data/hymenoptera_data/train/bees/2651621464_a2fa8722eb.jpg',\n",
              " './data/hymenoptera_data/train/bees/2053200300_8911ef438a.jpg',\n",
              " './data/hymenoptera_data/train/bees/2538361678_9da84b77e3.jpg',\n",
              " './data/hymenoptera_data/train/bees/760568592_45a52c847f.jpg',\n",
              " './data/hymenoptera_data/train/bees/198508668_97d818b6c4.jpg',\n",
              " './data/hymenoptera_data/train/bees/132826773_dbbcb117b9.jpg',\n",
              " './data/hymenoptera_data/train/bees/2765347790_da6cf6cb40.jpg',\n",
              " './data/hymenoptera_data/train/bees/2756397428_1d82a08807.jpg',\n",
              " './data/hymenoptera_data/train/bees/2477324698_3d4b1b1cab.jpg',\n",
              " './data/hymenoptera_data/train/bees/1097045929_1753d1c765.jpg',\n",
              " './data/hymenoptera_data/train/bees/2908916142_a7ac8b57a8.jpg',\n",
              " './data/hymenoptera_data/train/bees/2528444139_fa728b0f5b.jpg',\n",
              " './data/hymenoptera_data/train/bees/1092977343_cb42b38d62.jpg',\n",
              " './data/hymenoptera_data/train/bees/2452236943_255bfd9e58.jpg',\n",
              " './data/hymenoptera_data/train/bees/507288830_f46e8d4cb2.jpg',\n",
              " './data/hymenoptera_data/train/bees/2397446847_04ef3cd3e1.jpg',\n",
              " './data/hymenoptera_data/train/bees/2707440199_cd170bd512.jpg',\n",
              " './data/hymenoptera_data/train/bees/2227611847_ec72d40403.jpg',\n",
              " './data/hymenoptera_data/train/bees/2728759455_ce9bb8cd7a.jpg',\n",
              " './data/hymenoptera_data/train/bees/476347960_52edd72b06.jpg',\n",
              " './data/hymenoptera_data/train/bees/478701318_bbd5e557b8.jpg',\n",
              " './data/hymenoptera_data/train/bees/421515404_e87569fd8b.jpg',\n",
              " './data/hymenoptera_data/train/bees/2781170484_5d61835d63.jpg',\n",
              " './data/hymenoptera_data/train/bees/39672681_1302d204d1.jpg',\n",
              " './data/hymenoptera_data/train/bees/132511197_0b86ad0fff.jpg',\n",
              " './data/hymenoptera_data/train/bees/2467959963_a7831e9ff0.jpg',\n",
              " './data/hymenoptera_data/train/bees/2384149906_2cd8b0b699.jpg',\n",
              " './data/hymenoptera_data/train/bees/2601176055_8464e6aa71.jpg',\n",
              " './data/hymenoptera_data/train/bees/1807583459_4fe92b3133.jpg',\n",
              " './data/hymenoptera_data/train/bees/2364597044_3c3e3fc391.jpg',\n",
              " './data/hymenoptera_data/train/bees/92663402_37f379e57a.jpg',\n",
              " './data/hymenoptera_data/train/bees/266644509_d30bb16a1b.jpg',\n",
              " './data/hymenoptera_data/train/bees/2345177635_caf07159b3.jpg',\n",
              " './data/hymenoptera_data/train/bees/196658222_3fffd79c67.jpg',\n",
              " './data/hymenoptera_data/train/bees/3030772428_8578335616.jpg',\n",
              " './data/hymenoptera_data/train/bees/509247772_2db2d01374.jpg',\n",
              " './data/hymenoptera_data/train/bees/2959730355_416a18c63c.jpg']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADiGymTSlWqM",
        "outputId": "02e26af1-1b30-4634-a5fb-0bf4901fc8a8"
      },
      "source": [
        "# アリとハチの画像のDatasetを作成する\n",
        "\n",
        "\n",
        "class HymenopteraDataset(data.Dataset):\n",
        "    \"\"\"\n",
        "    アリとハチの画像のDatasetクラス。PyTorchのDatasetクラスを継承。\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "    file_list : リスト\n",
        "        画像のパスを格納したリスト\n",
        "    transform : object\n",
        "        前処理クラスのインスタンス\n",
        "    phase : 'train' or 'test'\n",
        "        学習か訓練かを設定する。\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, file_list, transform=None, phase='train'):\n",
        "        self.file_list = file_list  # ファイルパスのリスト\n",
        "        self.transform = transform  # 前処理クラスのインスタンス\n",
        "        self.phase = phase  # train or valの指定\n",
        "\n",
        "    def __len__(self):\n",
        "        '''画像の枚数を返す'''\n",
        "        return len(self.file_list)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        '''\n",
        "        前処理をした画像のTensor形式のデータとラベルを取得\n",
        "        '''\n",
        "\n",
        "        # index番目の画像をロード\n",
        "        img_path = self.file_list[index]\n",
        "        img = Image.open(img_path)  # [高さ][幅][色RGB]\n",
        "\n",
        "        # 画像の前処理を実施\n",
        "        img_transformed = self.transform(\n",
        "            img, self.phase)  # torch.Size([3, 224, 224])\n",
        "\n",
        "        # 画像のラベルをファイル名から抜き出す\n",
        "        if self.phase == \"train\":\n",
        "            label = img_path[30:34]\n",
        "        elif self.phase == \"val\":\n",
        "            label = img_path[28:32]\n",
        "\n",
        "        # ラベルを数値に変更する\n",
        "        if label == \"ants\":\n",
        "            label = 0\n",
        "        elif label == \"bees\":\n",
        "            label = 1\n",
        "\n",
        "        return img_transformed, label\n",
        "\n",
        "\n",
        "# 実行\n",
        "train_dataset = HymenopteraDataset(\n",
        "    file_list=train_list, transform=ImageTransform(size, mean, std), phase='train')\n",
        "\n",
        "val_dataset = HymenopteraDataset(\n",
        "    file_list=val_list, transform=ImageTransform(size, mean, std), phase='val')\n",
        "\n",
        "# 動作確認\n",
        "index = 0\n",
        "print(train_dataset.__getitem__(index)[0].size())\n",
        "print(train_dataset.__getitem__(index)[1])\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3, 224, 224])\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6M2pUtcklWqN"
      },
      "source": [
        "# DataLoaderを作成"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TwF8UealWqN",
        "outputId": "8c0a5ed3-8a7d-407c-bb72-0cc4be80648f"
      },
      "source": [
        "# ミニバッチのサイズを指定\n",
        "batch_size = 32\n",
        "\n",
        "# DataLoaderを作成\n",
        "train_dataloader = torch.utils.data.DataLoader(\n",
        "    train_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "val_dataloader = torch.utils.data.DataLoader(\n",
        "    val_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# 辞書型変数にまとめる\n",
        "dataloaders_dict = {\"train\": train_dataloader, \"val\": val_dataloader}\n",
        "\n",
        "# 動作確認\n",
        "batch_iterator = iter(dataloaders_dict[\"train\"])  # イテレータに変換\n",
        "inputs, labels = next(\n",
        "    batch_iterator)  # 1番目の要素を取り出す\n",
        "print(inputs.size())\n",
        "print(labels)\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 3, 224, 224])\n",
            "tensor([1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
            "        0, 0, 0, 0, 1, 0, 0, 1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDdKxowclWqN"
      },
      "source": [
        "# ネットワークモデルの作成する"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
          "referenced_widgets": [
            "350e40ad89fd4b289557201d12bd1878",
            "44a3efe233ad48699e9f59747f120f8d",
            "241cd1502c7c473ca0f525562f268c92",
            "96edb32a99d04f7d8bb9f161a699d4d3",
            "1b891dddf11343e696e5fd695c5a71df",
            "bc11034b13794c8db6b5dc2f4543932a",
            "d6b27aef63334910b4ec0b78b666684d",
            "af72d2b92986469d8abb55ab7b2ca180"
          ]
        },
        "id": "AsfGZ9B7lWqO",
        "outputId": "d7c303e3-b1e8-4229-ab39-c209cb820410"
      },
      "source": [
        "# 学習済みのVGG-16モデルをロード\n",
        "# VGG-16モデルのインスタンスを生成\n",
        "use_pretrained = True  # 学習済みのパラメータを使用\n",
        "net = models.vgg16(pretrained=use_pretrained)\n",
        "\n",
        "# VGG16の最後の出力層の出力ユニットをアリとハチの2つに付け替える\n",
        "net.classifier[6] = nn.Linear(in_features=4096, out_features=2)\n",
        "\n",
        "# 訓練モードに設定\n",
        "net.train()\n",
        "\n",
        "print('ネットワーク設定完了：学習済みの重みをロードし、訓練モードに設定しました')\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "350e40ad89fd4b289557201d12bd1878",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "ネットワーク設定完了：学習済みの重みをロードし、訓練モードに設定しました\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8rap7ISlWqO"
      },
      "source": [
        "# 損失関数を定義"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wiJCU6VlWqO"
      },
      "source": [
        "# 損失関数の設定\n",
        "criterion = nn.CrossEntropyLoss()"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kt3ziuoUlWqO"
      },
      "source": [
        "# 最適化手法を設定"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxkNa9LvlWqP",
        "outputId": "a480e08e-674e-45c3-c95d-b8c2ff848608"
      },
      "source": [
        "# 転移学習で学習させるパラメータを、変数params_to_updateに格納する\n",
        "params_to_update = []\n",
        "\n",
        "# 学習させるパラメータ名\n",
        "update_param_names = [\"classifier.6.weight\", \"classifier.6.bias\"]\n",
        "\n",
        "# 学習させるパラメータ以外は勾配計算をなくし、変化しないように設定\n",
        "for name, param in net.named_parameters():\n",
        "    if name in update_param_names:\n",
        "        param.requires_grad = True\n",
        "        params_to_update.append(param)\n",
        "        print(name)\n",
        "    else:\n",
        "        param.requires_grad = False\n",
        "\n",
        "# params_to_updateの中身を確認\n",
        "print(\"-----------\")\n",
        "print(params_to_update)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classifier.6.weight\n",
            "classifier.6.bias\n",
            "-----------\n",
            "[Parameter containing:\n",
            "tensor([[-0.0095,  0.0116,  0.0147,  ...,  0.0017,  0.0083,  0.0052],\n",
            "        [-0.0108,  0.0116, -0.0063,  ..., -0.0050,  0.0060,  0.0019]],\n",
            "       requires_grad=True), Parameter containing:\n",
            "tensor([-0.0021,  0.0016], requires_grad=True)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDfKFIDglWqP"
      },
      "source": [
        "# 最適化手法の設定\n",
        "optimizer = optim.SGD(params=params_to_update, lr=0.001, momentum=0.9)\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ocQ2VbJlWqP"
      },
      "source": [
        "# 学習・検証を実施"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXgRLEXHlWqP"
      },
      "source": [
        "# モデルを学習させる関数を作成\n",
        "\n",
        "\n",
        "def train_model(net, dataloaders_dict, criterion, optimizer, num_epochs):\n",
        "\n",
        "    # epochのループ\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch+1, num_epochs))\n",
        "        print('-------------')\n",
        "\n",
        "        # epochごとの学習と検証のループ\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                net.train()  # モデルを訓練モードに\n",
        "            else:\n",
        "                net.eval()   # モデルを検証モードに\n",
        "\n",
        "            epoch_loss = 0.0  # epochの損失和\n",
        "            epoch_corrects = 0  # epochの正解数\n",
        "\n",
        "            # 未学習時の検証性能を確かめるため、epoch=0の訓練は省略\n",
        "            if (epoch == 0) and (phase == 'train'):\n",
        "                continue\n",
        "\n",
        "            # データローダーからミニバッチを取り出すループ\n",
        "            for inputs, labels in tqdm(dataloaders_dict[phase]):\n",
        "\n",
        "                # optimizerを初期化\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # 順伝搬（forward）計算\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = net(inputs)\n",
        "                    loss = criterion(outputs, labels)  # 損失を計算\n",
        "                    _, preds = torch.max(outputs, 1)  # ラベルを予測\n",
        "                    \n",
        "  \n",
        "                    # 訓練時はバックプロパゲーション\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                    # イタレーション結果の計算\n",
        "                    # lossの合計を更新\n",
        "                    epoch_loss += loss.item() * inputs.size(0)  \n",
        "                    # 正解数の合計を更新\n",
        "                    epoch_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            # epochごとのlossと正解率を表示\n",
        "            epoch_loss = epoch_loss / len(dataloaders_dict[phase].dataset)\n",
        "            epoch_acc = epoch_corrects.double(\n",
        "            ) / len(dataloaders_dict[phase].dataset)\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0cuPPsxlWqQ",
        "outputId": "202ca950-75c7-4236-b7e1-b7cd3e55b688"
      },
      "source": [
        "# 学習・検証を実行する\n",
        "num_epochs=2\n",
        "train_model(net, dataloaders_dict, criterion, optimizer, num_epochs=num_epochs)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/5 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "-------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5/5 [01:22<00:00, 16.60s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "val Loss: 0.6903 Acc: 0.5621\n",
            "Epoch 2/2\n",
            "-------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [02:13<00:00, 16.70s/it]\n",
            "  0%|          | 0/5 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "train Loss: 0.4731 Acc: 0.7819\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5/5 [01:22<00:00, 16.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "val Loss: 0.1808 Acc: 0.9542\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NRfoFSZlWqQ"
      },
      "source": [
        "以上"
      ]
    }
  ]
}